Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection

This paper presents statistical signal processing approaches for clutter reduction in Stepped-Frequency Ground Penetrating Radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches are successfully evaluated and compared on real SF-GPR time-series. Field-test data are acquired using a monostatic S-band rectangular waveguide antenna. 1

A comparative and combined study of EMIS and GPR detectors by the use of Independent Component Analysis

Independent Component Analysis (ICA) is applied to classify unexploded ordnance (UXO) on laboratory UXO test-field data, acquired by stand-off detection. The data are acquired by an Electromagnetic Induction Spectroscopy (EMIS) metal detector and a ground penetrating radar (GPR) detector. The metal detector is a GEM-3, which is a monostatic sensor measuring the response of the environment on a multi-frequency constant wave excitation field (300 Hz to 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie antenna (500MHz to 2.5GHz). For both sensors the in-phase and the quadrature responses are measured at each frequency. The test field is a box of soil where a wide range of UXOs are placed at selected positions. The position and movement of both of the detectors are controlled by a 2D-scanner. Thus the data are acquired at well-defined measurement points. The data are processed by the use of statistical signal processing based on ICA. An unsupervised method based on ICA to detect, discriminate, and classify the UXOs from clutter is suggested. The approach is studied on GPR and EMIS data, separately and compared. The potential is an improved ability: to detect the UXOs, to evaluate the related characteristics, and to reduce the number of false alarms from harmless objects and clutter